A Durational Performance Using Visualized, Sonified, and Other Data Translation from Portable EEG Technologies in Mental and Physical Training
Bibliographic record
Abstract
In the moment of complete engagement in any activity, we function without conscious thought—referred to as ‘the zone.’ Digital technologies, from mobile devices to the Internet, can be a constant source of diversion; however, can digital tools help us get into the zone more quickly rather than simply distract us? Using open-source software and hardware, I have developed a real-time data visualization and sonification that have been recorded as performances on the website Mind & Matter, the project accompanying this paper. The performances are filmed in different locations and the visualization geolocates these locations, comparing them to the cell towers within the area. The project seeks to show waves within and around our body that are normally invisible. Each performance seeks to train both my brain and body to find stillness within. The paper is informed by the communications theorists and artists studied throughout the Communications and Culture program. I seek to answer Catherine Malabou’s question of “What We Should Do with Our Brains,” and how we might find agency in our brain plasticity though technological extension.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".